Nursing complexity, as described by nursing diagnoses, was shown to be associated with length of stay and mortality. These results should be confirmed after considering other variables through multivariate analyses. The concept of high-frequency/high-risk nursing diagnoses should be expanded in further studies.

Diagnostics is crucial for a prompt identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients, their isolation and treatment.

Purpose To investigate whether the number of nursing diagnoses on hospital admission is an independent predictor of the hospital length of stay. Design A prospective observational study was carried out. A sample of 2,190 patients consecutively admitted (from July to December 2014) in four inpatient units (two medical, two surgical) of a 1,547‐bed university hospital were enrolled for the study. Methods Data were collected from a clinical nursing information system and the hospital discharge register. Two regression analyses were performed to investigate if the number of nursing diagnoses on hospital admission was an independent predictor of length of stay and length of stay deviation after controlling for patients’ sociodemographic characteristics (age, gender), clinical variables (disease groupers, disease severity morbidity indexes), and organizational hospital variables (admitting inpatient unit, modality of admission). Findings The number of nursing diagnoses was shown to be an independent predictor of both the length of stay (β = .15; p < .001) and the length of stay deviation (β = .19; p < .001). Conclusions The number of nursing diagnoses is a strong independent predictor of an effective hospital length of stay and of a length of stay longer than expected. Clinical Relevance The systematic inclusion of standard nursing care data in electronic health records can improve the predictive ability on hospital outcomes and describe the patient complexity more comprehensively, improving hospital management efficiency.

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Aims: An available strategy to counteract academic failure is the development and implementation of student academic self-efficacy; however, to date, there are no instruments measuring it. The aim of this study was to develop and psychometrically test an academic nurse self-efficacy scale. Design: A longitudinal study design was used in accordance with Consensus-basedStandards for the Selection of health status Measurement Instruments guidelines. Methods:A convenience sample of 1,129 nursing students attending the first year of the course were involved. The data collection began in 2014 and went on for 3 years.Data were collected at the beginning of the first (T0), at the end of the first (T1), at the end of the second (T2), and at the end of the third (T3) year. The academic nurse self-efficacy scale was evaluated for content and face validity, for construct validity with explorative, confirmative factor analysis and hypothesis testing and for reliability. The standard error and the smallest detectable difference were also evaluated.Results: Scree plot analysis suggested a four-factor solution and confirmative factor analysis model reached a good fit. We verified the first hypothesis, partially the second and not the third. The dimensions show a Cronbach's α 0.72-0.83. The smallest detectable difference was 26%. Conclusions:The academic nurse self-efficacy scale had good validity and reliability and should be considered for nursing students. Impact: These findings may have an impact on universities, mainly in nursing degree programmes because nurse educators can identify nursing students with low academic self-efficacy and help them in their academic duties. Indirectly, academic self-efficacy monitoring can be used for evaluating the effect of different teaching strategies or mentorship support over time. K E Y W O R D S academic performance, academic success, education nursing, educational measurement, instrument development, nurses, nursing, nursing graduate, self-efficacy | 399 BULFONE Et aL.

Overall, knowledge regarding influenza, implications during pregnancy and influenza vaccine was poor among pregnant women. In Italy, the National Vaccine Prevention Plan 2012-2014 recommends influenza vaccine during pregnancy, but only 18/309 were aware of this recommendation. These results suggest that in order to increase influenza vaccine acceptance it is necessary to improve pregnant women knowledge about influenza and to offer education to healthcare providers.

Seasonal influenza vaccination (SIV) of health-care workers (HCWs) is recommended in most countries to protect them and their patients from infection. Although SIV can reduce the risk of influenza complications among vulnerable patients, vaccination uptake is generally unsatisfactory. The present study aimed to assess the impact of different programs in promoting SIV uptake among HCWs during the season 2017/2018 in four teaching hospitals in Rome. A multicentric cross-sectional study was carried out, in order to describe the four different campaigns and to assess their impact by identifying and developing a set of indicators that provide information about the vaccination services, the percentage of invited HCWs, the vaccinators' workforce and the vaccination coverage rates.The hospitals organized different strategies: Hospital 1, 3 and 4 organized educational courses for HCWs and actively invited every single HCW through e-mail. All the hospitals organized a dedicated unit for influenza vaccination, and Hospital 1 added on-site vaccination sessions that required a large number of staff. Hospital 1 and hospital 4 registered a comparable vaccination coverage rate, 12.97% and 12.76%, respectively, while it was 6.88% in Hospital 2 and 4.23% in Hospital 3. Our indicators demonstrated to be effective and useful for analyzing the different SIV campaigns. The results suggest that the best practice to promote SIV among HCWs should include multiple approaches. Among those, an easy access to the vaccination site seems to play a key role in determining a higher vaccination coverage.

Introduction: Social isolation increases in the over-74 population and it is a risk factor for death and Long Term Care (LTC) use. In order to prevent the negative consequences of social isolation on this population community interventions focused on strengthening the social network should be intensified. The aim of this paper is to describe the impact on health care use of a Communitybased pro-Active Monitoring Program (CAMP) providing phone monitoring to all the clients and home visits according to the individual's needs. Methodology: In order to provide an evaluation of the program outcomes, the rates of clients' hospitalization and admissions to Long Term Care facilities during 2011 have been assessed. The observed rates have been compared with expected ones calculated on available information for similar population. A cost-analysis has been also carried out to analyze the program sustainability. Results: The studied sample is made up by 1408 over-74 citizens followed up during 2011 in Rome (Italy) by CAMP. The cumulative observation time was 1362 p/y; 61 individuals died during 2011 (death rate 4.3%). The hospital admission rate observed among CAMP's clients was 254‰ (357/1408; CL95% ± 91‰), lower than the 282‰ reported for the over-74 population of Rome. This translates into 39 averted hospitalization. The LTC admission rate is also reduced among CAMP's clients (9/1,408, 6.6‰ CL95% ± 0.8‰ vs. 9.7‰ reported for a comparable sample); it translates into 4 averted LTC admissions. The averted cost * Corresponding author. M. C. Marazzi et al. 188 ranged between 47,153 € and 220,117 € according to the range of services used by the clients, which translates into a percentage of estimated cost reduction on yearly basis ranged between 3% and 12.5% of the whole cost of services used by the studied population. Discussion: The paper suggests the capacity of CAMP to reduce both the over-74 hospitalization rate and use of LTC. Cost analysis also indicates a cost reduction as a consequence of the CAMP implementation. Further studies including a control group and a detailed cost-benefit analysis are needed to check the program sustainability on larger population.